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全景地标:在牙科教育中比较LLM辅助,手动追踪和自主学习

Suresh Kandagal Veerabhadrappa¹, Jayanth Kumar Vadivel², Seema Yadav Roodmal³

¹Department of Oral Diagnostic Sciences, Faculty of Dentistry, SEGi University, Petaling Jaya, Selangor, Malaysia.

International dental journal

|January 23, 2026

概括

手动追踪 (MT) 对牙科学生学习全景放射图上的解剖学地标最有效. 虽然ChatGPT在特定领域显示了改进,但结合MT和AI的综合方法可能会提高熟练程度.

科学领域:

牙科放射学教育牙科放射学教育
解剖学地标识别识别方法

背景情况:

在全景放射图上准确识别解剖学标志在牙科中至关重要,但具有挑战性.
传统的教学方法往往需要额外的工具来培养技能.
这项研究比较了自导学习 (SDL),手动跟踪 (MT) 和人工智能 (ChatGPT) 对于学习放射性地标.

研究的目的:

评估和比较三种补充学习模式的有效性,以识别全景放射图上的解剖学地标.
在牙科放射学教育中确定自主学习,手动跟踪和人工智能辅助学习的有效性.
评估不同学习方法对牙科学生识别特定放射性地标的能力的影响.

主要方法:

一项前性研究涉及63名第三年牙科学生,分为三组 (SDL,MT,ChatGPT).
学生接受了讲座,并立即 (基线) 进行测试,并在4周后进行随访.
使用非参数统计测试分析了群内和群间的差异.

主要成果:

与SDL和ChatGPT组相比,手动追踪 (MT) 组的整体得分明显更高.
与基线相比,MT组在24个基点和ChatGPT组在16个基点中观察到显著改善.
聊天GPT小组在识别四个特定的里程碑方面表现优于MT,其中包括斜腿过程.

结论:

手动追踪 (MT) 是最有效的整体方法,用于学习全景射线图上的解剖学地标.
聊天GPT在学习特定的地标方面表现出价值,这表明AI可以发挥补充作用.
建议采用综合方法,将传统的手动跟踪与人工智能辅助学习相结合,以加强牙科放射学教育.

关键词:

人工智能的人工智能是人工智能.牙科牙科牙科牙科教育的可持续性教育的可持续性标志性的地标标志性的地标全景射线图 (Panoramic Radiography) 是一个全景射线图.追踪追踪追踪追踪

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